Comments on: Visualization, modeling, and surpriseshttp://www.johndcook.com/blog/2013/02/07/visualization-modeling-and-surprises/ Singular Value ConsultingFri, 09 Dec 2016 00:10:10 +0000hourly1By: Visualizzazione dei dati e modellazione | Evolutivo CRM and more...http://www.johndcook.com/blog/2013/02/07/visualization-modeling-and-surprises/comment-page-1/#comment-731311 Tue, 08 Mar 2016 08:37:46 +0000http://www.johndcook.com/blog/?p=12844#comment-731311[…] Ecco il link: http://www.johndcook.com/blog/2013/02/07/visualization-modeling-and-surprises/ […] ]]>By: Visual Revelations, Howard Wainer | Civil Statisticianhttp://www.johndcook.com/blog/2013/02/07/visualization-modeling-and-surprises/comment-page-1/#comment-489 Fri, 22 Mar 2013 02:05:34 +0000http://www.johndcook.com/blog/?p=12844#comment-489[…] p. 80: “a reasonable strategy in what ought to be an iterative process. Sometimes one has a data-related question and then draws a graph to try to answer it. After drawing the graph a new question might suggest itself, and hence a different graph, better suited to this new question (perhaps with additional data), is drawn. This in turn suggests something else, and so on, until either the data or the grapher is exhausted. […] My experience suggests that if you begin with a general-purpose plot there is a greater chance of finding what you had not expected.” This is my experience as well, and reminds me also of Hadley Wickham’s description of statistics as iterating between models and graphics. […] ]]>By: Andrew Gelmanhttp://www.johndcook.com/blog/2013/02/07/visualization-modeling-and-surprises/comment-page-1/#comment-486 Sat, 09 Feb 2013 19:27:33 +0000http://www.johndcook.com/blog/?p=12844#comment-486John:

Statistics without model checking makes me uneasy, to put it mildly. Some argue that model checking is less important in Bayesian statistics, but I don’t buy that. If anything, because Bayesian analysis makes it easier to construct complex models, there may be more need for model checking.

Yes, this is what we are trying to get at in Bayesian Data Analysis. You iterate the following 3 steps: (1) model building, (2) inference conditional on the model, (3) model checking. The better you do (1) and (2), the more informative step (3) will be.

The paradox, if there is one, is that people tend to think of steps 2 and 3 as competing: in step 2 you (temporarily) commit to a belief, whereas in step 3 you look for problems. I think these go together–really, that’s what the scientific method is all about–but I’ve found that, in many cases, people who spend a lot of time with a model don’t want to check it, while people who spend a lot of time on exploratory data analysis don’t like models at all.